ICORES 2024 Abstracts

Area 1 - Methodologies and Technologies
Full Papers Short Papers
Area 2 - Applications
Full Papers Short Papers

Area 1 - Methodologies and Technologies

Full Papers
Paper Nr: 9
Title:

A Neurodynamic Duplex for Distributionally Robust Joint Chance-Constrained Optimization
Authors:

Siham Tassouli and Abdel Lisser
Abstract: This paper introduces a new neurodynamic duplex approach to address distributionally robust joint chance-constrained optimization problems. We assume that the constraints’ row vectors are independent, and their probability distributions belong to a specific distributional uncertainty set that is not known beforehand. Within our study, we examine two uncertainty sets for these unknown distributions. Our framework’s key feature is the use of a neural network-based method to solve distributionally robust joint chance-constrained optimization problems, achieving an almost sure convergence to the optimum without relying on standard state-of-the-art solving methods. In the numerical section, we apply our proposed approach to solve a profit maximization problem, demonstrating its performance and comparing it against existing state-of-the-art methods.
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Paper Nr: 15
Title:

Investigation of Workforce Dynamical Behaviour from a Phase Plane Perspective
Authors:

Timo Lahteenmaa-Swerdlyk and François-Alex Bourque
Abstract: The purpose of this work was to investigate the population dynamics of on-the-job training. The ratio of mentees to mentors was considered, and its effect on overwhelming (saturating) the training system was analysed when undergoing a growth phase between two healthy states. This analysis was completed by analytically solving a simplified continuous model of the problem with constant input parameters. The model was investigated through a phase-plane interpretation, or the mentee versus mentor population as the system evolves. The key input parameter of the analysis was the saturation limit: the ratio of mentees to mentors above which the system becomes saturated. This value can be modified by adjusting various factors such as the quality, quantity, and/or the delivery method of the training. Of special interest was the time for the system to evolve as the saturation limit was varied. It was discovered that the system behaviour can fall into three categories based on its value. If the saturation limit is very low, the system will remain saturated and never reach a steady state. If the threshold is very high, the system will remain unsaturated (healthy) and reach steady state at inputted target populations, albeit in a relatively slow timeframe. Finally, for a particular middle range of values, the system will reach steady state at inputted target populations in an optimal time by crossing into and out of saturation. Therefore, finding the optimal values for the input parameters will depend on a compromise between reaching the target state quickly and not exceeding the target population levels, which will depend on the priorities of the organization. Given that any occupation with on-the-job training could experience such effects during a transition, understanding the dynamics of saturation is thus essential to design an efficient training system.
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Paper Nr: 20
Title:

Integration of Sustainable Production Criteria into Production Scheduling: A Systematic Search and a Critical Review
Authors:

María S. Cavallieri, Elisabeth Viles and Jairo R. Montoya-Torres
Abstract: Production scheduling plays a pivotal role in shaping and optimizing production processes to promote sustainability in manufacturing companies. Understanding how current studies consider sustainable production criteria in scheduling objectives can help companies transition from a reactive to a proactive production mode. This paper presents a systematic and critical analysis of 120 articles to examine the extent to which sustainable production criteria have been applied to scheduling problems in manufacturing systems. The analysis categorizes articles based on the type of scheduling problem, problem formulation, resolution method, and sustainability aspects considered, while also tracking the evolution of each sustainability indicator to identify trends. The study reveals the use of diverse sustainability indicators in production scheduling. Indicators such as "Makespan" and "Energy consumption" are prevalent, while social indicators related to employee well-being and safety are still emerging and rarely considered. Notable gaps identified in this review include the absence of real-world applications, unclear criteria for indicator selection, and limited holistic assessments linking production improvements to overall sustainability. The review emphasizes the need for practical and strategic approaches, serving as a guide for the manufacturing sector and informing future research directions.
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Paper Nr: 22
Title:

Toward a Global Constraint for Minimizing the Flowtime
Authors:

Camille Bonnin, Arnaud Malapert, Margaux Nattaf and Marie-Laure Espinouse
Abstract: This work is a study toward a global constraint minimizing the flowtime of a single machine scheduling problem. Classical methods for filtering algorithms use a lower bound coming from the solution of a relaxation. Notably, there are several polynomial relaxations to minimize the flowtime on a single machine. A general scheme for the global constraint is proposed that allows the use of a subset of polynomial relaxations that lays the ground for more complex filtering algorithms. The filtering algorithm has a complexity of O(n· M · R), where n is the number of tasks, M is an upper bound on the time windows of these tasks, and R is the complexity of the algorithm used for solving the relaxation. The constraint has been tested on both single machine and flowshop problems. Experimental results show that the performance improvement depends on the type of problem. The number of branches reduction is promising for designing new filtering rules.
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Paper Nr: 23
Title:

Stochastic Single-Allocation Hub Location Routing Problem for the Design of Intra-City Express Systems
Authors:

Yuehui Wu, Hui Fang, Ali G. Qureshi and Tadashi Yamada
Abstract: The paper concentrates on designing an intra-city express system in a practical environment. In the target networks, flows of parcels are exchanged between branch offices via a less-than-truckload hub-and-spoke network in a stochastic environment. Hub and vehicle capacities are considered, and the flows between all pairs of branch offices are assumed to be stochastic variables. The problem is modelled as a multi-stage recourse model, named capacitated single-allocation hub location routing problem with stochastic demands (CSAHLRPSD). A sample average approximation (SAA) framework is proposed, in which two variants of adaptive large neighbourhood search algorithms are used to solve the SAA problem and to calculate the recourse cost. The SAA framework is tested on benchmark instances, proving that it can efficiently deal with the CSAHLRPSD. Also, the results indicate that employing the CSAHLRPSD can cut the operation cost in comparison with the deterministic model in the practical and stochastic environment.
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Paper Nr: 26
Title:

Equilibrium Analysis and Social Optimization of a Selectable Single or Time-Based Batch Service
Authors:

Ayane Nakamura and Tuan Phung-Duc
Abstract: In current transportation systems, a common road is shared by multiple types of vehicles with different capacities. To consider this phenomenon, we propose a model in which customers can strategically select a single or batch service, and then receive a common service in a single-server queue with exponential service times. Customers potentially arrive at the system according to a Poisson process and choose whether to join the queue directly or wait for a batch service. The batch service commences periodically according to a Poisson process and the capacity of the batch is determined by a geometric distribution. The optimization of such a model has not been studied despite being an important social issue. We derive the unique equilibrium strategy of customers, socially optimal strategy, and socially optimal relationship of fees for both services. Furthermore, we demonstrate that these optimal fees exhibit linear relationships. In terms of practical application, this system will allow us to consider the effects of road congestion on transportation platforms.
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Paper Nr: 32
Title:

A Revisited Branch and Bound Method for the Weighted Safe Set Problem
Authors:

Alberto Boggio Tomasaz and Roberto Cordone
Abstract: The Weighted Safe Set Problem requires to partition an undirected graph into two families of connected components, respectively denoted as safe and unsafe, in such a way that each safe component dominates the unsafe adjacent components with respect to a weight function. We introduce some improvements to an existing exact approach that produce a significant reduction in the effort required to find the optimum or in the gap between the optimum and the best solution obtained within a given time limit. The first improvement consists of a relaxation that is weaker than the original one, but allows to adopt a more effective branching strategy and stronger variable fixing procedures. The second one is the integration of a dedicated heuristic in the exact approach. The experimental results show a strong average reduction of the computational time and the number of branching nodes. This also mitigates the anticipated termination of the algorithm due to the exhaustion of the memory on the largest benchmark instances.
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Paper Nr: 54
Title:

Multi-Server Queue, with Heterogeneous Service Valuations Induced by Travel Costs
Authors:

Itzhak Moshkovitz, Irit Nowik and Yair Shaki
Abstract: This work presents a variation of Naor’s strategic observable model (Naor, 1969) for a loss system M/G/2/2, with a heterogeneous service valuations induced by the location of customers in relation to two servers, A, located at the origin, and B, located at M. Customers incur a “travel cost” which depends linearly on the distance of the customer from the server. Arrival of customers is assumed to be Poisson with a rate that is the integral of a nonnegative intensity function. We find the Nash equilibrium threshold strategy of the customers, and formulate the conditions that determine the optimal social welfare strategy. For the symmetric case (i.e., both servers have the same parameters and the intensity function is symmetric), we find the socially optimal strategies; Interestingly, we find that when only one server is idle, then under social optimality, the server also serves far away consumers, consumers whom he would not serve if he was a single server (i.e., in M/M/1/1).
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Paper Nr: 59
Title:

Hybrid Manufacturing / Remanufacturing Inventory Model with Two Markets and Price Sensitive Demands with Competition
Authors:

Matthieu Godichaud and Lionel Amodeo
Abstract: A hybrid system that produces new and remanufactured products on a common line for two distinct markets is studied in this article. We consider price sensitive demands with competition between the two types of products. The problem is to maximize a profit as a function of economic production quantities and demands or prices decisions. The resulting model is a mixed nonlinear model with linear and nonlinear constraints. A mathematical analysis is proposed to develop an efficient resolution approaches. Numerical study shows that sequential decisions, determining demands first and reorder intervals after, provides solutions closed to optimum. Sensitivity analysis highlights the importance of demand parameters compared to those related to inventory and the importance of considering all costs, not just setup and holding costs, when evaluating order or production quantities.
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Paper Nr: 61
Title:

Partition-Form Cooperative Games in Two-Echelon Supply Chains
Authors:

Gurkirat Wadhwa, Tushar S. Walunj and Veeraruna Kavitha
Abstract: Competition and cooperation are inherent features of any multi-echelon supply chain. The interactions among the agents across the same echelon and that across various echelons influence the percolation of market demand across echelons. The agents may want to collaborate with others in pursuit of attracting higher demand and thereby improving their own revenue. We consider one supplier (at a higher echelon) and two manufacturers (at a lower echelon and facing the customers) and study the collaborations that are ‘stable’; the main differentiator from the existing studies in supply chain literature is the consideration of the following crucial aspect – the revenue of any collaborative unit also depends upon the way the opponents collaborate. Such competitive scenarios can be modeled using what is known as partition form games. Our study reveals that the grand coalition is not stable when the product is essential and the customers buy it from any of the manufacturers without a preference. The supplier prefers to collaborate with only one manufacturer, the one stronger in terms of market power; further, such collaboration is stable only when the stronger manufacturer is significantly stronger. Interestingly, no stable collaborative arrangements exist when the two manufacturers are nearly equal in market power.
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Paper Nr: 62
Title:

Serial or Simultaneous? Possible Attack Strategies with an Arsenal of Attack Tools
Authors:

Yahel Giat and Irit Nowik
Abstract: In various fields such as medicine, management, cyber operations, and military strategy, the choice between sequential and parallel strategies is pivotal in achieving objectives, be it maximizing the likelihood of success or minimizing the time to victory. This study considers a hacker who attempts to destroy a rival system using multiple attacking tools. It is assumed that the success probability of each attack tool to destroy the system is equal and independent of the other tools. Execution of an attack is time consuming and it is assumed that this attack time increases exponentially with the number of tools used simultaneously. We consider different attaching schemes that vary in their design and balance between parallel and sequential steps. Our findings indicate that when the attack time for a multi-tool attack is extremely short, the optimal solution will be a purely simultaneous attack. Conversely, if the attack time approaches the total time required for a sequential attack, then the optimal solution will be a purely sequential approach. In between these extremes, we discover that a mixed strategy is optimal. Interestingly, our numerical analysis reveals that in these mixed cases, it is consistently more advantageous to initiate a simultaneous attack and then complement it with a sequential one. Moreover, we demonstrate that as the probability of success increases, the optimum tends towards a sequential attack.
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Paper Nr: 80
Title:

Integrating Reliability and Sustainability: A Multi-Objective Framework for Opportunistic Maintenance in Closed-Loop Supply Chain
Authors:

Abdelhamid Boujarif, David W. Coit, Oualid Jouini, Zhiguo Zeng and Robert Heidsieck
Abstract: Closed-loop supply chains are at the forefront of sustainable industrial practices since they promote the reuse of products through remanufacturing, recycling, and repair operations. Within this framework, repair centers are increasingly considered an alternative source of spare parts. This creates a need to enhance repair operations to balance the reliability of repaired parts with sustainability. This paper, developed in collaboration with GE Healthcare, presents a multi-objective framework incorporating spare part reliability post-repair estimation into opportunistic maintenance decisions. This research uses real-world data and advanced modeling techniques to refine maintenance strategies and provide a comprehensive solution that acknowledges component interdependencies. By employing NSGA-III, the paper seeks to develop a decision-support mechanism that recommends the proactive replacement of components, thereby enhancing the quality of repaired spare parts.
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Short Papers
Paper Nr: 10
Title:

Multi-Criteria Service System Designing Using Tabu Search Method
Authors:

Marek Kvet and Jaroslav Janáček
Abstract: Designing a good public service system that provides a geographical region with service through a specified number of service centers is a very difficult task, particularly when multiple quality evaluation criteria are applied. A Pareto front of public service system designs is a very useful instrument for any designer who must consider multiple requests from public representatives. Due to the computational difficulty of determining the Pareto front, a number of heuristic approaches have been developed. One of these techniques, gradual refinement, proved to be quite effective, but its performance could be enhanced by eliminating the repetition of some rudimentary swap routines. This contribution focuses on the application of tabu search features to enhance and increase the efficacy of the gradual refinement process by suspending the routines’ few useful applications. The resulting metaheuristic is validated through numerical experimentation using benchmarks, and the approximations of the Pareto front are compared to the exact Pareto fronts.
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Paper Nr: 11
Title:

Pareto Front Approximation by Ant Colony Optimization
Authors:

Jaroslav Janáček and Marek Kvet
Abstract: The Pareto frontier of multi-objective problem solutions denotes the unique exact solution to a problem with two or more equivalent objectives. Even when the number of problem solutions is finite, determining the precise Pareto frontier is a difficult task. Different metaheuristics can therefore provide a user with a decent approximation of the Pareto frontier in a reasonable amount of time, whereas the exact computational time-intensive approaches cannot. The acceptable computational time of metaheuristics counterbalances a solution’s deviation from the Pareto frontier. This contribution describes one of a spectrum of metaheuristics implemented with the objective of locating non-dominated solutions to the public service system design problem involving two competing criteria. The metaheuristic minimizes the difference between the present set of non-dominated solutions and the Pareto front by applying the ant colony optimization principle. A series of numerical experiments with benchmarks for which the exact Pareto frontiers are known are used to evaluate the efficacy of the proposed metaheuristic. Even though the proposed method is applicable anywhere, the used dataset comes from an Emergency Medical Service system in Slovakia, which belongs to the generally known wide class of public service systems.
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Paper Nr: 13
Title:

Innovation Project Selection Considering Stochastic Weighted Product Model
Authors:

Guilherme B. Marcondes and Claudia B. Marcondes
Abstract: Nowadays, organizations and companies are increasingly seeking to innovate in products and processes. In general, innovation materializes in the form of project execution. However, such projects are complex to implement, the evaluation of which requires understanding and knowledge of many factors. This makes its selection also difficult, given that it is necessary to experiment, which may or may not be successful, involving volatility and uncertainty, which increases when the open approach is implemented (to compensate for a lack of information, resources and skills). The selection of innovation projects can be supported by appropriate tools, in order to assist the decision maker in their choices. Multi-criteria decision methods (MCDM) can provide this support, especially if they take uncertainty into account. This work proposes the application of the Weighted Product Model (WPM), an MCDM, in the selection of innovation projects. To address uncertainty, assessments performed by more than one expert are translated into three-point estimates and Monte Carlo simulation applied for a stochastic approach. The proposed method is applied to the selection in a group of 13 innovation projects, as an example.
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Paper Nr: 21
Title:

Integration of Pricing and Production Scheduling Decisions: A Mathematical Model
Authors:

J. Mhanna and H. Nouinou
Abstract: In today’s competitive manufacturing landscape, achieving operational efficiency and optimizing revenue generation are key objectives for make-to-order manufacturers. This paper presents a novel approach for integrating production scheduling and pricing decisions in a make-to-order manufacturing environment. We propose a comprehensive mathematical model that addresses the complex interplay between production scheduling and pricing strategies. By jointly optimizing these two critical aspects, manufacturers can enhance their competitiveness and profitability. The objective of the scheduling decisions is to minimize the total tardiness penalties of jobs on a non-preemptive parallel machine environment, a widely used measure of customer service. Pricing decisions on the other hand aim at maximizing the total revenue. A mixed integer linear programming model is formulated and an algorithm is developed based on the e-constraint approach to conduct the experimental analysis. The algorithm aims to find the best solutions, from the decision-maker’s perspective, by iteratively adjusting production schedules and pricing decisions.
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Paper Nr: 24
Title:

Two-Stage Adaptable Robust Optimization for Glass Production
Authors:

Anton Medvedev, Safia Kedad-Sidhoum and Frédéric Meunier
Abstract: In the glass industry, visual and thermal properties of the glass sheets are obtained via the deposit of thin layers of different materials. A standard way to perform this step is the use of a “magnetron,” in which the materials are transferred from cathodes to the sheets using a magnetic field. Since the cathodes are very expensive, activation and replacement decisions have to be carefully decided to keep the cost of the wasted materials low. The production is organized in campaigns and the activation and replacement decisions of the cathodes have to be taken before each campaign. Yet, the exact orders to process during a campaign are only revealed after the decisions have been taken. We focus here on the case of two campaigns, which we model as a two-stage robust optimization problem. We propose a method based on the finite adaptability approach of Bertsimas and Caramanis (2010) combined with the branch-and-bound of Subramanyam et al. (2020). Experiments on real instances show that our method leads to clear diminutions of the cost of wasted material in the worst cases, and—even more interesting—allow to find solutions for cases that are unfeasible with the heuristic used by the practitioners.
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Paper Nr: 25
Title:

Modeling Missing Maritime Objects Using an Agent Based Model
Authors:

Jarrod Grewe and Igor Griva
Abstract: Accurate modeling the movement and behaviors of missing persons and vessels is critical in their finding and rescuing in maritime environments. Current methods focus on using particle techniques that model several factors including leeway and drift but lack the ability to model human factors and behaviors. This research explores the idea of using an agent-based approach to model missing objects with the goal of developing a methodology that accounts for missing person behavior in a maritime domain. This new approach leads to a more accurate missing persons movement trajectories and results in finding better search plans. The results show that an agent-based model can consider environmental elements, behavioral factors, and hazards when modeling target movement in a maritime domain which is critical in missing object modeling. The developed approach also shows how an agent-based model can help find optimal search plans.
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Paper Nr: 27
Title:

A Bounded Multi-Vacation Queue Model for Multi-Stage Sleep Control
Authors:

Jie Chen
Abstract: To evaluate the performance of multi-stage sleep telecommunication systems, this paper presents a bounded multi-vacation queue model. The energy consumption predicted by this model, shows an average error rate of 0.0177 and the delay (predicted by the same model) shows an average error rate of 0.0655. Both error rates were calculated over 99 instances.A general algorithmic method integrating the analytical model further demonstrates the model’s accuracy.
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Paper Nr: 31
Title:

An Efficient Approximate Dynamic Programming Approach for Resource-Constrained Project Scheduling with Uncertain Task Duration
Authors:

Alireza Etminaniesfahani, Hanyu Gu, Leila M. Naeni and Amir Salehipour
Abstract: The resource-constrained project scheduling problems (RCPSP) with uncertainties have been widely studied. The existing approaches focus on open-loop task scheduling, and only a few research studies develop a dynamic and adaptive closed-loop policy as it is regarded as computationally time-consuming. In this paper, an approximate dynamic programming (ADP) approach is developed to solve the RCPSPs with stochastic task duration (SRCPSP). The solution from a deterministic average project is utilised to reduce the computational burden associated with the roll-out policy, and a parameter is introduced in the roll-out policy to control the search strength. We test the proposed approach on 960 benchmark instances from the well-known library PSPLIB with 30 and 60 tasks and compare the results with the state-of-the-art algorithms for solving the SRCPSPs. The results show that our average-project-based ADP (A-ADP) approach provides competitive solutions in a short computational time. The investigation of the characteristics of the instances also discloses that when resources are tight, it is more important to intensify the search in the roll-out policy.
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Paper Nr: 37
Title:

Scheduling Onboard Tasks of the NIMPH Nanosatellite
Authors:

Julien Rouzot, Joséphine Gobert, Christian Artigues, Romain Boyer, Frédéric Camps, Philippe Garnier, Emmanuel Hebrard and Pierre Lopez
Abstract: In the context of terrestrial space missions, thanks to the recent development of micro and nanotechnologies, nanosatellites are becoming increasingly popular for their lower cost and ease of deployment. The NIMPH (Nanosatellite to Investigate Microwave Photonics Hardware) mission is an ongoing academic project aimed at developing and launching such a nanosatellite. The onboard resources on these missions are often very limited, and in our study case, a single onboard computer is responsible for orchestrating the science and avionic tasks of the nanosatellite. These tasks are subject to various constraints, such as frequency, minimum/maximum delay between the execution of the same type of task and strict precedences. This makes the scheduling of the onboard tasks a challenging problem, which is critical for the mission success. In this paper, we tackle the problem of scheduling NIMPH onboard tasks using Constraint Programming methods. Our scheduler demonstrates its performance by generating optimal or near-optimal schedules for the NIMPH nanosatellite.
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Paper Nr: 38
Title:

Evolutionary-Based Ant System Algorithm to Solve the Dynamic Electric Vehicle Routing Problem
Authors:

Simon Caillard and Rachida Ben Chabane
Abstract: This article addresses the Dynamic Electric Vehicle Routing Problem with Time Windows (DEVRPTW) using a hybrid approach blending genetic and Ant Colony Optimization (ACO) algorithms. It employs an Ant System algorithm (AS) with an integrated memory system that undergoes mutations for solution diversification. Testing on Schneider instances under static and dynamic conditions, with run time of 10 and 3 minutes respectively, reveals promising results. Compared to static solutions, deviations of 8.55% and 2.38% are observed in vehicle count and total distance. In a dynamic context, the algorithm maintains proximity to static results, with 10.99% and 4.41% deviations in vehicle count and distance. Instances R1 and R2 present challenges, suggesting potential improvements in memory and pheromone transfer during re-optimization.
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Paper Nr: 39
Title:

Heuristic Methods for the Antenna-Constrained Beam Layout Optimization on Multibeam Broadcasting Mission
Authors:

Camille Lescuyer, Christian Artigues, Jean-Thomas Camino and Cédric Pralet
Abstract: In this paper, we tackle a payload design problem for a broadcasting mission where a telecommunication satellite must provide television services to distinct regions defined as polygons. To cover these polygons, several telecommunication beams are emitted by the satellite, with the risk that they mutually degrade their performance while also being hard to accommodate mechanically on the spacecraft. The problem is to determine a set of non-conflicting beams that cover all the regions and optimize a performance metric related to the sizes of the beams used. The first method is a matheuristic exploiting iterative solv of an ILP model. The second method, called the merge-and-split heuristic, is inspired by Iterated Local Search and reuses a fast graph coloring algorithm to analyze conflicts among selected beams. These two methods are evaluated on realistic instances, the largest one involving more than one hundred regions to cover.
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Paper Nr: 43
Title:

Generalized Maximum Capacity Path Problem with Loss Factors
Authors:

Javad Tayyebi, Mihai-Lucian Rîtan and Adrian M. Deaconu
Abstract: The focus of this paper is on an extension of the maximum capacity path problem, known as the generalized maximum capacity path problem. In the traditional maximum capacity path problem, the objective is to find a path from a source to a sink with the highest capacity among all possible paths. However, this extended problem takes into account the presence of loss factors in addition to arc capacities. The generalized maximum capacity path problem is regarded as a network flow optimization problem, where the network comprises arcs with both capacity constraints and loss factors. The main goal is to identify a path from the source to the sink that allows for the maximum flow along the path, considering the loss factors while satisfying the capacity constraints. The paper introduces a zero-one formulation for the generalized maximum capacity path problem. Additionally, it presents two efficient polynomial-time algorithms that can effectively solve this problem.
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Paper Nr: 45
Title:

Variable Neighborhood Search for the Electric Bus Charging Stations Location Design Problem
Authors:

Michal Koháni and Stanislav Babčan
Abstract: In the paper we describe a mathematical model and propose a solution method for solving the electric bus charging station’s location design problem. We formulate a location-scheduling mathematical model, where the set of possible charging stations can be in terminals and depots and tours of all vehicles are known and will be unchanged. To solve the problem, we propose solving method based on the Variable Neighbourhood Search metaheuristic. Using the proposed method, we realised extensive numerical experiments on the test datasets created from real operational data provided by the municipal transport operator in the city of Zilina.
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Paper Nr: 46
Title:

Scheduling Single AGV in Blocking Flow-Shop with Identical Jobs
Authors:

Erik Boom, Matúš Mihalák, Frank Thuijsman and Mark M. Winands
Abstract: We consider a flow-shop with m stations (machines) and n identical jobs that need to be processed on each station. The processing time of every job on station i is p_i. After a job is processed on a station i, it needs to be transported by an automated guided vehicle (AGV) to the next station i+1. There is only one AGV. We assume no buffers, i.e., when the AGV transports a job to a station, the station needs to be empty. Furthermore, an AGV can transport at most one job at a time, non-preemptively, i.e., it cannot leave the job in the middle of transportation. The transportation times between the stations are given and are independent of whether the AGV carries a job or not. We study the problem of scheduling the single AGV such that all jobs are processed and the makespan is minimized. We provide a characterization of feasible schedules, and use it to derive an integer linear program (ILP) for the problem. We observe that solving the ILP requires a rather large amount of computation time even for very small instances. We use the ILP-formulation to design a rolling-window based heuristic that scales up and provides close-to-optimum schedules, as demonstrated by experimental evaluation that also involves comparison to two natural greedy algorithms.
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Paper Nr: 53
Title:

Balancing Resources and Demand: A Bi-Objective Mixed-Integer Programming Approach of Healthcare Districts in Chile
Authors:

Paulette Castillo, Victor Bucarey, Sebastián Davila and Franco Quezada
Abstract: In the search for equitable and efficient health service delivery, geographical partition into operational districts is a fundamental factor. This research delves into the intricate challenge of combinatorial optimization of healthcare districts, with an application to the Metropolitan region of Santiago, Chile, where growing population pressures exacerbate concerns about the distribution of healthcare resources. By emphasizing continuity from primary to secondary healthcare levels, we underline the importance of a good district plan, considering key parameters such as population homogeneity, compactness, and alignment between capacity and demand. By applying a mixed integer linear programming model with a bi-objective function, our findings indicate substantial scope for improving resource allocation, potentially cutting overages by up to 38.77% at the primary healthcare level and up to 15% at the secondary healthcare level.
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Paper Nr: 55
Title:

The multi-Depot multiple Set Orienteering Problem: An Integer Linear Programming Formulation
Authors:

Ravi Kant and Abhishek Mishra
Abstract: In this article, we introduce a novel variant of the single Depot multiple Set Orienteering Problem (sDmSOP), which we refer to as the multi-Depot multiple Set Orienteering Problem (mDmSOP). We suggest the integer linear program (ILP) of the mDmSOP also, and analyze the impact of the Sub-tour Elimination Constraints (SECs) based on the Miller–Tucker–Zemlin (MTZ) and the Gavish-Graves (GG) model on it. The mDmSOP is most frequently encountered in distribution logistics. In mDmSOP, a fleet of travelers is utilized to serve a set of customers from a number of depots, with each traveler associated with a specific depot. The challenge is to choose the routes for each traveler to maximize the profit within a specific budget, while the profit can be earned from a set of customers only once by visiting exactly one customer. We show the simulation results conducted on the General Algebraic Modeling System (GAMS) 39.0.2, which is used to model and analyze linear, non-linear, mixed-integer, and other complex optimization problems. The Generalized Traveling Salesman Problem (GTSP) instances of up to 200 vertices are taken as the input data set for the simulations. The results show that the MTZ-based formulation takes less time than the GG-based formulation to converge to the optimal solution for the mDmSOP.
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Paper Nr: 60
Title:

A Supervised Machine Learning Approach for the Vehicle Routing Problem
Authors:

Sebastian Ammon, Frank Phillipson and Rui J. Almeida
Abstract: This paper expands on previous machine learning techniques applied to combinatorial optimisation problems, to approximately solve the capacitated vehicle routing problem (VRP). We leverage the versatility of graph neural networks (GNNs) and extend the application of graph convolutional neural networks, previously used for the Travelling Salesman Problem, to address the VRP. Our model employs a supervised learning technique, utilising solved instances from the OR-Tools solver for training. It learns to provide probabilistic representations of the VRP, generating final VRP tours via non-autoregressive decoding with beam search. This work shows that despite that reinforcement learning based autoregressive approaches have better performance, GNNs show great promise to solve complex optimisation problems, providing a valuable foundation for further refinement and study.
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Paper Nr: 76
Title:

A Learning Powered Bi-Level Approach for Dynamic Electricity Pricing
Authors:

Patrizia Beraldi, Luigi Gallo and Alessandra Rende
Abstract: This paper presents a comprehensive approach to electricity tariff determination by integrating advanced Artificial Intelligence (AI) techniques with Bi-Level (BL) optimization. More specifically, AI techniques are used to obtain accurate forecasts of photovoltaic panel generation, which are then used as input parameters for a deterministic BL problem that models the interaction between a power supplier and a residential prosumer. To handle the high complexity of the BL formulations, the model is first reformulated into a single-level structure, and then linearized using an approach based on the application of the dual reformulation. An intensive experimental phase is carried out on a real case study to test the effectiveness of the proposed methodology and to quantify the impact of the forecast techniques on the supplier strategy.
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Paper Nr: 34
Title:

Assessment of the Academic Load in a Curriculum Through an Optimization Model: Case Study of a Master Program
Authors:

Myriam Gaete and Marcela C. González-Araya
Abstract: The evaluation of academic load is necessary and constitutes a fundamental process in the design and redesign of programs. This is because an excessive academic load can have academic consequences such as lag, as well as effects on mental health, including depression, anxiety, burnout, self-esteem problems, among others. Academic load is a complex and dynamic topic, resulting in the absence of a single approach to its study and measurement. In this sense, this work proposes a mathematical model of linear programming. The case study evaluated in Magister, a Chilean university. The results reveal an even distribution of academic load between semesters and courses within the program. As the semesters progress, the academic load tends to increase gradually. Integrated courses, such as Course 10 and Course 11, have higher loads compared to others. In the third semester there is variability in the academic load, with one course concentrating most of the study hours. In total, 294 hours of study are required to complete the program. A comprehensive review of academic load distribution is recommended to ensure an equitable and manageable educational experience for students.
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Paper Nr: 44
Title:

An Alternative Robust Design to Assist a Single-Objective Performance Optimization: Simulation Analysis of a Flexible Manufacturing System
Authors:

Wa-Muzemba A. Tshibangu
Abstract: During the lockdowns following the Covid-19 pandemic many companies have become flexible by implementing new manufacturing technologies, such as group technology (GT), just-in-time (JIT) production systems, and flexible manufacturing systems (FMSs) that, hence, become among the solutions of the future. This paper uses the emergence of these systems to present an alternative robust design formulation to Taguchi methodology before proposing a single-objective optimization scheme to find the optimal operational settings of primary individual key performance indicators (KPIs). The study uses the Throughput Rate (TR) and the Mean Flow Time (MFT) as illustrative examples of KPIs, tracked over a range of AGV fleet sizes. Additional KPIs, e.g., Work-in-process (WIP), Machine utilization, and AGV utilization are also analyzed as secondary measures to validate and fine-tune the results of the procedure. The study deploys and uses in association multiple statistical tools for a proper analysis and validation of the technique. The effectiveness of the proposed model is validated by comparing the results to some other similar approaches. Although derived from simulation of manufacturing operations, the framework presented in this paper can be applied to various industries including food production, financial institutions, warehouse industry, and healthcare.
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Paper Nr: 77
Title:

Deep Transfer Learning for Installed Base Life-Cycle Evolution Forecast
Authors:

Emna Turki, Oualid Jouini, Ziad Jemai and Robert Heidsieck
Abstract: In Healthcare industry, companies are reducing their environmental impact by implementing a closed loop supply chain (CLSC) in which products can be de-installed and bought back for reconditioning or parts reuse. In this supply chain, it is necessary to implement the appropriate strategies to ensure a sustainable parts management system knowing that the installed base (IB) evolution and the products design changes are highly impacting factors. Since strategic CLSC decisions are taken early in the part and/or product life-cycles, usu-ally there is not enough data to predict the IB information. Therefore, We build a Deep Transfer learning framework to forecast the products IB evolution from the beginning to the end-of-life (EOL) using data of different generations from the same product family. We provide a use case from a Healthcare company showing the performance of different deep learning models on a long horizon.
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Area 2 - Applications

Full Papers
Paper Nr: 12
Title:

Dynamic Modeling and Effective Inventory Management for Uncertain Perishable Supply Chains with non Synchronized Internal Dynamics
Authors:

Beatrice Ietto and Valentina Orsini
Abstract: The problem we consider is to define an efficient management strategy for a periodic-review production-inventory system whose dynamics is characterized by various elements of complexity. These elements characterize the vast majority of practical situations and consist of: 1) perishable goods with unknown deterioration rate, 2) an uncertain future customer demand with oscillations that are not statistically modelable, 3) non-synchronized operations of goods stocking and dispatching inside each review period. With reference to this problem we define a general model of supply chain dynamics capturing the mentioned complex features and encompassing, as particular cases, other models proposed in the literature. We also provide a coherent two criteria based evaluation of the Bullwhip Effect (BE) and a Replenishment Policy (RP) resulting from a min-max formulation of Model Predictive Control (MPC) approach. The RP maximizes the satisfied customer demand and contains the BE within precise limits independent of the customer demand randomness.
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Paper Nr: 17
Title:

Naval Fleet Schedule Optimization Using an Integer Linear Program
Authors:

Megan Widmer, Michèle Fee and François-Alex Bourque
Abstract: To inform decisions about future fleet planning, a way to model asset availability over time is needed. To accomplish this, a tool was developed that generates optimized fleet schedules from simplified operations and maintenance cycles. By repeating these cycles and offsetting them from asset to asset, it is possible to generate schedules that meet a set of fleet availability requirements. Target schedule characteristics were encoded in an Integer Linear Program (ILP) and solved using the PuLP python package with the COIN-OR branch and cut solver. To evaluate the effectiveness of the approach, fleet schedules for notional asset fleets were generated and compared qualitatively to those made using a genetic algorithm (GA) based tool that is currently in use. The ILP tool was found to produce schedules that met the requirements more consistently than the GA.
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Paper Nr: 28
Title:

Packing-Inspired Algorithms for Periodic Scheduling Problems with Harmonic Periods
Authors:

Josef Grus, Claire Hanen and Zdeněk Hanzálek
Abstract: We tackle the problem of non-preemptive periodic scheduling with a harmonic set of periods. Problems of this kind arise within domains of periodic manufacturing and maintenance and also during the design of industrial, automotive, and avionics communication protocols, where an efficient static schedule of messages is crucial for the performance of a time-triggered network. We consider the decision variant of the periodic scheduling problem on a single highly-utilized machine. We first prove a bijection between periodic scheduling and a particular 2D packing of rectangles (we name it height-divisible 2D packing). We formulate the problem using Constraint Programming and compare it with equivalent state-of-the-art Integer Linear Programming formulation, showing the former’s superiority on difficult instances. Furthermore, we develop a packing-inspired first fit heuristic, which we compare with methods described in the literature. We justify our proposed methods on problem instances inspired by the communication of messages on one channel.
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Paper Nr: 35
Title:

Real-Time Bus Arrival Prediction: A Deep Learning Approach for Enhanced Urban Mobility
Authors:

Narges Rashvand, Sanaz S. Hosseini, Mona Azarbayjani and Hamed Tabkhi
Abstract: In urban settings, bus transit stands as a significant mode of public transportation, yet faces hurdles in delivering accurate and reliable arrival times. This discrepancy often culminates in delays and a decline in ridership, particularly in areas with a heavy reliance on bus transit. A prevalent challenge is the mismatch between actual bus arrival times and their scheduled counterparts, leading to disruptions in fixed schedules. Our study, utilizing New York City bus data, reveals an average delay of approximately eight minutes between scheduled and actual bus arrival times. This research introduces an innovative, AI-based, data-driven methodology for predicting bus arrival times at various transit points (stations), offering a collective prediction for all bus lines within large metropolitan areas. Through the deployment of a fully connected neural network, our method elevates the accuracy and efficiency of public bus transit systems. Our comprehensive evaluation encompasses over 200 bus lines and 2 million data points, showcasing an error margin of under 40 seconds for arrival time estimates. Additionally, the inference time for each data point in the validation set is recorded at below 0.006 ms, demonstrating the potential of our Neural-Net based approach in substantially enhancing the punctuality and reliability of bus transit systems.
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Short Papers
Paper Nr: 16
Title:

Integrating Memory-Based Perturbation Operators into a Tabu Search Algorithm for Real-World Production Scheduling Problems
Authors:

Manuel Schlenkrich, Michael Bögl, Anna Gattinger, Ionela Knospe and Sophie N. Parragh
Abstract: Production scheduling problems, arising in real-world use cases, are characterized by a very large number of operations and complex constraints. In order to handle such problems, practical solution approaches need to be generic enough, to capture all relevant restrictions, while being able to calculate good solutions in a short amount of time. Metaheuristic methods, especially combinations of trajectory and population-based approaches, are promising techniques to meet this criterion. In this work, we develop a framework for deriving and integrating memory-based perturbation operators into a highly flexible Tabu Search algorithm for scheduling problems, in order to enhance its overall performance. The perturbation operators are inspired by evolutionary algorithms and collect valuable solution information during the Tabu Search procedure via an elite solution pool. This information is used in a destroy-and-repair step integrated into the Tabu Search procedure, aiming to preserve promising solution structures. We investigate several parameters and perform computational experiments on job-shop benchmark instances from literature, as well as on a real-world industry use case. Integrating the developed memory-based perturbation operators into the Tabu Search algorithm leads to significant performance improvements on the real-world problem. The benchmark evaluations demonstrate the robustness of the approach, when dealing with sensitive parameters.
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Paper Nr: 30
Title:

A Sequential Heuristic for the Efficient Management of a Work Center’s Stocking Area
Authors:

Fabrizio Marinelli and Andrea Pizzuti
Abstract: In our partnership with a leading company specializing in automatic cutting machines for reinforcement processes, we address the management of a work center whose optimization calls for the solution of four distinct subproblems. Focusing on the third one, the subproblem asks for the effective packing of items on the identical buffers of a stocking area. The items arrive divided into subgroups (i.e., patterns), are associated with orders, and have time windows. We devise an SVC heuristic that efficiently determines feasible packing solutions while simultaneously minimizing the number of used buffers, lowering operations and fragmented orders. The SVC incorporates the idea of reachable points to restrict the location sets on the buffers. The experimental campaign highlights the SVC’s effectiveness in achieving optimality for small realistic instances, with a specific emphasis on reducing fragmented orders. Additionally, the approach showcased its ability to explore the multi-objective space and demonstrated scalability in solving practical instances.
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Paper Nr: 57
Title:

Finite Interval Processes: Simulating Military Operations
Authors:

Robert M. Bryce
Abstract: We discuss extending Poisson point processes to finite interval processes, in the context of simulating military operations over time. Conceptually the extension is simple, points are extended to finite duration of length equal to the operations’ duration. We discuss effects of finite duration, namely lowering of set frequencies (and therefore artificially reduced expected demand) due to the extension of points to intervals—and its correction. The Canadian Armed Forces (CAF) has a Force Structure Readiness Assessment (FSRA+) Monte Carlo simulation tool that implements a finite interval process, and the results here offer insight on the approach.
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Paper Nr: 63
Title:

Toward Pareto-Optimal Investment Mix to Achieve Carbon Neutrality: A Case Study
Authors:

Bedor Alyahya, Alexander Brodsky and Gregory Farley
Abstract: Leading institutions are committing to climate action by setting greenhouse gas emission (GHG) reduction targets to reach carbon neutrality. Institutional stakeholders are faced with the challenge of achieving these objectives through cost-effective investments in diverse and interconnected infrastructures, all while considering the existing infrastructure. In our prior work, we tackled this issue by creating an extensible investment model and tool known as GADGET - Green Assessment and Decision GuidancE Tool - to provide Pareto-optimal investment recommendations for heterogeneous infrastructures within energy service networks. In this study we employ GADGET to provide actionable investment recommendations to George Mason University’s stakeholders on a mix of interrelated infrastructures for its Fairfax campus with the initial scope considering renewable energy certificates and carbon offsets, gas and electric boilers, on site solar panels, energy storage, and Dominion Virginia Energy contract schedule. Our analysis covers the period from 2025 to 2050, considering investment decisions at five-year intervals, with the overarching goal of achieving carbon neutrality by 2040. We also perform sensitivity analysis of recommended mixes to understand the implications of fluctuating REC and offset prices on projected outcomes. While our recommendations are specifically designed for George Mason University, the insights we provide may also be relevant and beneficial to other academic institutions.
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Paper Nr: 48
Title:

Evolutionary Techniques for the Nurse Scheduling Problem
Authors:

Mehdi Sadeghilalimi, Malek Mouhoub and Aymen Ben Said
Abstract: The Nurse Scheduling Problem (NSP) is a combinatorial optimization problem that creates weekly scheduling solutions for nurses. These solutions must satisfy constraints for the workload coverage requirements while optimizing one or more objectives related to hospital costs or nurses’ preferences. Although exact methods may be used to solve the NSP and return the optimal solution, they usually come with an exponential time cost. Therefore, approximate methods may be considered as they offer a good trade-off between the quality of the solution and the running time. In this context, we propose a solving method based on Genetic Algorithms (GAs) to solve the NSP. To evaluate the efficiency of our proposed method, we conducted experiments on various NSP instances. Further, we compared the quality of the returned solutions against solutions obtained from exact methods and metaheuristics. The experimental results reveal that our proposed method can fairly compete with B&B in terms of the quality of the solution while delivering the solutions in much faster running times.
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Paper Nr: 74
Title:

Economic Sustainability in Last-Mile Drone Delivery Problem with Fulfillment Centers: A Mathematical Formulation
Authors:

Maria E. Bruni, Sara Khodaparasti and Guido Perboli
Abstract: This study is motivated by the increasing growth in the competitive e-commerce market where on-line business-to-customer retailers, seeking for cost-efficient and timely delivery services, are persuaded to adopt a drone-based delivery system against the traditional terrestrial one. Recently, the drone-aided delivery services have significantly been eased, thanks to the development of fulfilment centers, as aerial base stations monitoring and arranging back-and-forth drone trips between the fulfilment centers and customers’ sites that also provide the retailers with services such as package handling, restocking, and drone recharge. Obviously, adopting a drone-based delivery system incurs various expenses: besides drone recharge, maintenance, and energy consumption costs, the usage cost for fulfillment centers, namely tariff, should be paid by the retailers to the FC manager. This study aims to address the economic sustainability of a drone-aided delivery system with fulfillment centers and to provide the retailer with an optimal delivery plan maximizing his profit. This could eventually provide a stable platform for new and small-sized business-to-customer retailers trying to survive in such a competitive market and to promote the use of drone fulfillment centers.
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